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Hits 1 – 2 of 2
1
HittER: Hierarchical Transformers for Knowledge Graph Embeddings ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Chen, Sanxing
;
Gao, Jianfeng
;
Ji, Yangfeng
;
Jiao, Jian
;
Liu, Xiaodong
;
Zhang, Ruofei
. - : Underline Science Inc., 2021
Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.812/ Abstract: This paper examines the challenging problem of learning representations of entities and relations in a complex multi-relational knowledge graph. We propose HittER, a Hierarchical Transformer model to jointly learn Entity-relation composition and Relational contextualization based on a source entity's neighborhood. Our proposed model consists of two different Transformer blocks: the bottom block extracts features of each entity-relation pair in the local neighborhood of the source entity and the top block aggregates the relational information from outputs of the bottom block. We further design a masked entity prediction task to balance information from the relational context and the source entity itself. Experimental results show that HittER achieves new state-of-the-art results on multiple link prediction datasets. We additionally propose a simple approach to integrate HittER into BERT and demonstrate its effectiveness on two ...
Keyword:
Computational Linguistics
;
Information Extraction
;
Language Models
;
Machine Learning
;
Machine Learning and Data Mining
;
Natural Language Processing
URL:
https://dx.doi.org/10.48448/y853-5214
https://underline.io/lecture/37925-hitter-hierarchical-transformers-for-knowledge-graph-embeddings
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2
A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-SQL Parsing ...
The 28th International Conference on Computational Linguistics 2020
;
Chen, Sanxing
;
Ji, Yangfeng
. - : Underline Science Inc., 2020
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